Statistics 2 Part 1

Course Description

Statistics 2 Part 1 is an engaging, intermediate-level course offered by the prestigious London School of Economics (LSE) as part of their MicroBachelors program in Statistics Fundamentals. This self-paced course is designed to build upon the foundational concepts introduced in Statistics 1, focusing on developing your understanding of elementary statistical theory. With a particular emphasis on probability and distribution theory, this course will equip you with essential skills applicable to econometrics, finance, and quantitative social science.

What Students Will Learn

By the end of this course, you will have:

• Expanded on key ideas from Statistics 1, making them accessible to students with moderate mathematical competence.
• Mastered the application of standard statistical operators.
• Developed the ability to recall and understand a variety of well-known distributions and their respective moments.
• Gained a solid foundation in probability theory, random variables, and multivariate random variables.
• Acquired skills that serve as a springboard for more specialized undergraduate-level courses in statistics and econometrics.

Pre-requisites

While there are no formal prerequisites for Statistics 2 Part 1, it is strongly recommended that students complete Statistics 1, Parts 1 and 2 beforehand. This ensures a solid foundation in statistical concepts, as the subject matter is cumulative in nature. A moderate level of mathematical competence is beneficial for understanding and applying the course material.

Course Content

• Probability theory I
• Probability theory II
• Random variables
• Common distributions of random variables
• Multivariate random variables

Who This Course Is For

This course is ideal for:

• Students pursuing a career in data analysis, economics, finance, or social sciences
• Professionals looking to enhance their statistical knowledge for career advancement
• Individuals interested in developing a deeper understanding of statistical concepts and their real-world applications
• Anyone seeking to build a strong foundation for further studies in econometrics and advanced statistics

Real-World Applications

The skills acquired in this course have wide-ranging applications in various fields:

1. Finance: Apply probability theory and distribution analysis to assess investment risks and make informed decisions.
2. Economics: Use statistical models to analyze economic trends and forecast market behavior.
3. Social Sciences: Employ statistical techniques to interpret survey data and draw meaningful conclusions from research studies.
4. Data Science: Leverage probability and distribution theory to clean, analyze, and interpret large datasets.
5. Business: Make data-driven decisions by applying statistical concepts to market research and consumer behavior analysis.
6. Research: Design and conduct experiments with a solid understanding of statistical hypothesis testing.